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MiniMax M2.5 vs GLM 5

MiniMax M2.5

MiniMax

78#69
vs
GLM 5

Zhipu AI

78#70
Signal-by-Signal Comparison
SignalMiniMax M2.5DeltaGLM 5
Capabilities
67
--
67
Benchmarks
79
-1
81
Pricing
99
+2
98
Context window size
84
--
84
Recency
100
--
100
Output Capacity
88
+3
85
Overall Result
2 wins
of 6
1 wins
MiniMax M2.5 wins 2 of 6 signals

Score History

Score History (25 data points)
MiniMax M2.5GLM 5
MiniMax M2.5

78

current score

Leader

Tied

right now

GLM 5

78

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

MiniMax M2.5

MiniMax

Best Value
Per request$0.000600
Daily$2.00
Monthly$60.00
Annual$720.00

GLM 5

Zhipu AI

Per request$0.002225
Daily$7.42
Monthly$222.50
Annual$2670.00

MiniMax M2.5 saves you $162.50/month

That's $1950.00/year compared to GLM 5 at your current usage level of 100K calls/month.

73% cheaper
Choose MiniMax M2.5 for cost optimization

MiniMax M2.5 pricing:
Input:$0.15/M tokens
Output:$0.90/M tokens
GLM 5 pricing:
Input:$0.95/M tokens
Output:$2.55/M tokens
Tie
MiniMax M2.5

MiniMax

78

Composite Score

Tie
GLM 5

Zhipu AI

78

Composite Score

Signal-by-Signal Comparison
MetricMiniMax M2.5GLM 5Winner
Overall Score
78
78
--
Rank#69#70
MiniMax M2.5
Quality Rank#69#70
MiniMax M2.5
Adoption Rank#69#70
MiniMax M2.5
Parameters------
Context Window205K205K--
Pricing$0.15/$0.90/M$0.95/$2.55/M--
Signal Scores
Capabilities
67
67
MiniMax M2.5
Benchmarks
79
81
GLM 5
Pricing
99
98
MiniMax M2.5
Context window size
84
84
MiniMax M2.5
Recency
100
100
MiniMax M2.5
Output Capacity
88
85
MiniMax M2.5
Benchmark Head-to-Head(3 benchmarks)
MiniMax M2.5: 0GLM 5: 2
MiniMax M2.5
GLM 5
Normalized 0-100%
MMLU-Pro
80.1%86%
SWE-bench Verified
75.8%-
Arena Elo
13901457
Benchmark Interpretation

Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.

MiniMax M2.5Strong Performer

Scores 78/100 (rank #69), placing it in the top 77% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
GLM 5Strong Performer

Scores 78/100 (rank #70), placing it in the top 76% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.

When to Use Each Model

Choose MiniMax M2.5 when you need:

  • High-volume production workloads where API costs must be minimized
  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model

Choose GLM 5 when you need:

  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model
Cost-Performance Analysis
MiniMax M2.5Best Value
Input cost$0.15/M tokens
Output cost$0.90/M tokens
Cost per quality point$0.013
Est. monthly (1M tokens/day)$15.75
GLM 5
Input cost$0.95/M tokens
Output cost$2.55/M tokens
Cost per quality point$0.045
Est. monthly (1M tokens/day)$52.50

MiniMax M2.5 offers 70% better value per quality point. At 1M tokens/day, you'd spend $15.75/month with MiniMax M2.5 vs $52.50/month with GLM 5 - a $36.75 monthly difference.

Latency & Speed
MiniMax M2.5Faster
Speed score0/100
GLM 5
Speed score0/100

Both models have comparable response speeds. For most applications, the latency difference is negligible.

When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

MiniMax M2.5

Customer support chatbot

Suitable for user-facing chat with competitive response times. MiniMax M2.5 also offers lower per-token costs for high-volume support

MiniMax M2.5

Long document analysis

Larger context window (205K tokens) can process longer documents, contracts, and research papers in a single pass

MiniMax M2.5

Batch data extraction

Lower output pricing ($0.90/M) reduces costs when processing thousands of records daily

MiniMax M2.5

Creative writing & content

Higher overall composite score (78/100) correlates with better nuance, coherence, and style in long-form content

MiniMax M2.5
Which Should You Choose?
Our recommendation:
MiniMax M2.5

MiniMax M2.5 and GLM 5 are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.

MiniMax M2.5
Recommended

by MiniMax

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • Choose for Cost - 70% lower pricing; better value at scale
  • Choose for Reliability - Higher uptime and faster response speeds
  • Choose for Prototyping - Stronger community support and better developer experience
  • Choose for Production - Wider enterprise adoption and proven at scale

by Zhipu AI

Consider for specialized use cases.

Capability Comparison
CapabilityMiniMax M2.5GLM 5
Vision (Image Input)
Function Calling
Streaming
JSON Mode
Reasoning
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

MiniMax M2.5

MiniMax

Best Value
$1.35
estimated monthly cost

GLM 5

Zhipu AI

$4.77
estimated monthly cost

MiniMax M2.5 saves you $3.42/month

That's 72% cheaper than GLM 5 at 1,000 tokens/request and 100 requests/day.

Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.

Parameters & Context
ParameterMiniMax M2.5GLM 5
Context Window205K205K
Max Output Tokens196,608131,072
Open SourceYesYes
CreatedFeb 12, 2026Feb 11, 2026
Last updated: 6m ago

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